This is the 3rd blog in our healthcare series on “Data Privacy in Healthcare and the Role of Technology.” This blog series deep-dives into data privacy and transparency in the healthcare industry. It explores in detail the compliance and disclosure requirements in the global pharmaceutical industry and the international laws and regulations that guide them.
This blog series also discusses the traditional manual methods of anonymization currently prevalent, how industry 4.0 solutions can automate and vastly improve conventional anonymization, and how Gramener’s AInonymize solution can transform clinical trial disclosures and regulatory compliance in the healthcare industry.
This article further delves into the role of data privacy and confidentiality in healthcare, data privacy laws in different countries, how EMA 0070 and Health Canada PRCI balance privacy with transparency, and how anonymization tech can help.
Check out other parts of the series:
Table of Contents
Nefarious attacks against information systems and persistent cyber threats to peddle hacked data to unscrupulous bidders have recently been rising. The invasion of patient privacy is a growing concern in big data analytics, posing critical challenges to many organizations.
Data privacy allows access to information based on privacy laws and policies. This determines who can view confidential, medical, financial, and personal data and information.
Confidentiality in healthcare requires those with access to patient records to ensure that their trust is not breached or broken.
Forbes magazine reported an incident where the Target Corporation sent baby care coupons to a teenage girl, much to her parents’ surprise. To prevent such incidents, developers must ensure that their applications adhere to privacy agreements.
They also must ensure that the said sensitive information does not inadvertently become public or fall into the wrong hands because of product updates or changes in privacy regulations.
Medical data privacy is critical, and care should be taken to protect it at all costs.
Healthcare organizations must safeguard and manage personal information. They also must address the legal responsibilities and risks when processing personal data, ensuring that they comply with the pertinent data protection legislation.
Countries differ in their laws and policies toward data privacy. The following are some key takeaways of data protection laws and regulations enforced across countries.
The HIPAA Act/ Patient Safety and Quality Improvement Act (PSQIA)/ HITECH Act
The European Medicines Agency (EMA) Policy 0070 and the General Data Protection Regulation (GDPR)
Health Canada Public Release of Clinical Information (PRCI)
The Data Protection Act (DPA)
Federal Law on Personal Data
IT Act and IT (Amendment) Act
Constitution
Clinical trial transparency is beneficial across the board, be it to patients, the scientific community, regulators, clinical trial sponsors, or trial participants.
Market authorization requires the disclosure of anonymized trial information. This includes clinical study document publication under Health Canada’s Public Release of Clinical Information (PRCI) and the European Medicines Agency (EMA) Policy 0070.
Reusing and sharing data brings new life to trial data, supporting health research, furthering trial transparency, and earning stakeholder trust while relieving the trial subjects’ burden. Simultaneously, if the unethical use of data erodes trust, even if you remove the identifying information of the trial participants to protect privacy.
To protect the privacy of trial participants before reusing or sharing trial data, trial sponsors anonymize the data. For example, as per Health Canada’s PRCI and EMA’s Policy 0070, trial sponsors must anonymize clinical study documents before publishing them on Health Canada or EMA data access portals.
Quantitative risk-based or statistical anonymization measures how easy it is to re-identify individuals through data such as medical event dates, medical history, and demographic information.
It then uses various data transformations, such as suppressing or removing outlier values in the data, generalizing demographic values or disease classifications, or shifting dates to reduce the likelihood of reidentification.
The continuous increase in data transformation reduces the identifiability of the information till it reaches a point below the applicable anonymization threshold. At this level, the data can no longer be identified.
This threshold is determined based on regulatory guidance, industry benchmarks, and data disclosure precedents. For example, both Health Canada and EMA recommend a threshold of 0.09. This is equivalent to having a minimum of 11 similarly looking individuals in every group based on the adopted and well-understood concept of k-anonymity and cell-size rules.
International regulatory standards like Health Canada and EMA 0070 have established specific guidelines to anonymize clinical data. This will help the pharma community further healthcare research by sharing trial data without compromising patient privacy.
Pharmaceutical companies need help to publish clinical documents on time and in an affordable fashion while protecting patient data privacy. It takes weeks or even months to anonymize unstructured data manually.
Worse, human intervention vastly increases the chances of error, leading to heavy fines and penalties resulting from the reidentification of patient information or data breaches.
Gramener’s AI-nonymize solution can boost a healthcare team’s efficiency, meet stringent regulatory policies and minimize reidentification risks. It has already helped clients save annual costs of up to $ 1 Mn and reduce turnaround time from a staggering 45 days to just seven days.
In this blog, we explored what data privacy and confidentiality means in healthcare and the laws governing them. We also discussed Health Canada and EMA 0070 in the context of transparency and privacy and how tech has the potential to transform anonymization in healthcare.
Stay tuned for the next blog in the series, where we will show how data privacy protection techniques can safeguard patient data. We will also discuss the different protection techniques and how AI and ML solutions can augment them.
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